Abnormal Trajectory Detection Based on Geospatial Consistent Modeling
Anomalous trajectory detection plays a significant role in fraud detection and adverse events monitoring for ride-hailing services. The spatial and temporal dynamics of road networks and the sparsity of trajectories make anomalous trajectory detection a challenging task. Most existing methods are ba...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9214405/ |
| _version_ | 1818479749242028032 |
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| author | Haiquan Wang Jiachen Feng Leilei Sun Kaiqiang An Guoping Liu Xiang Wen Runbo Hu Hua Chai |
| author_facet | Haiquan Wang Jiachen Feng Leilei Sun Kaiqiang An Guoping Liu Xiang Wen Runbo Hu Hua Chai |
| author_sort | Haiquan Wang |
| collection | DOAJ |
| description | Anomalous trajectory detection plays a significant role in fraud detection and adverse events monitoring for ride-hailing services. The spatial and temporal dynamics of road networks and the sparsity of trajectories make anomalous trajectory detection a challenging task. Most existing methods are based on density and isolation approaches, which ignore geographical information. Motivated by these challenges and shortcomings, we propose a novel method, which considers geospatial constraints of the trajectories and avoids sparsity issues. In our method, the geographical information and topological constraints of trajectories are embedded into structured vector space. Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are used to model common trajectory features. Our method could identify anomalous trajectories and determine which parts are responsible for anomalies by using these features. Experiments on two real-world datasets have been conducted, and results demonstrate the effectiveness and feasibility of the proposed method. |
| first_indexed | 2024-12-10T11:14:52Z |
| format | Article |
| id | doaj.art-7c5026aacb1b4eababc9cbfcaadbd638 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T11:14:52Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7c5026aacb1b4eababc9cbfcaadbd6382022-12-22T01:51:14ZengIEEEIEEE Access2169-35362020-01-01818463318464310.1109/ACCESS.2020.30288479214405Abnormal Trajectory Detection Based on Geospatial Consistent ModelingHaiquan Wang0https://orcid.org/0000-0003-1745-9814Jiachen Feng1https://orcid.org/0000-0002-9859-1966Leilei Sun2https://orcid.org/0000-0002-0157-1716Kaiqiang An3https://orcid.org/0000-0003-3695-5143Guoping Liu4Xiang Wen5Runbo Hu6Hua Chai7College of Software, Beihang University, Beijing, ChinaCollege of Software, Beihang University, Beijing, ChinaNLSDE Laboratory, Beihang University, Beijing, ChinaDidi Chuxing, Beijing, ChinaDidi Chuxing, Beijing, ChinaDidi Chuxing, Beijing, ChinaDidi Chuxing, Beijing, ChinaDidi Chuxing, Beijing, ChinaAnomalous trajectory detection plays a significant role in fraud detection and adverse events monitoring for ride-hailing services. The spatial and temporal dynamics of road networks and the sparsity of trajectories make anomalous trajectory detection a challenging task. Most existing methods are based on density and isolation approaches, which ignore geographical information. Motivated by these challenges and shortcomings, we propose a novel method, which considers geospatial constraints of the trajectories and avoids sparsity issues. In our method, the geographical information and topological constraints of trajectories are embedded into structured vector space. Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are used to model common trajectory features. Our method could identify anomalous trajectories and determine which parts are responsible for anomalies by using these features. Experiments on two real-world datasets have been conducted, and results demonstrate the effectiveness and feasibility of the proposed method.https://ieeexplore.ieee.org/document/9214405/Anomalous trajectory detectiontrajectory embeddinggeospatial consistentdeep neural networks |
| spellingShingle | Haiquan Wang Jiachen Feng Leilei Sun Kaiqiang An Guoping Liu Xiang Wen Runbo Hu Hua Chai Abnormal Trajectory Detection Based on Geospatial Consistent Modeling IEEE Access Anomalous trajectory detection trajectory embedding geospatial consistent deep neural networks |
| title | Abnormal Trajectory Detection Based on Geospatial Consistent Modeling |
| title_full | Abnormal Trajectory Detection Based on Geospatial Consistent Modeling |
| title_fullStr | Abnormal Trajectory Detection Based on Geospatial Consistent Modeling |
| title_full_unstemmed | Abnormal Trajectory Detection Based on Geospatial Consistent Modeling |
| title_short | Abnormal Trajectory Detection Based on Geospatial Consistent Modeling |
| title_sort | abnormal trajectory detection based on geospatial consistent modeling |
| topic | Anomalous trajectory detection trajectory embedding geospatial consistent deep neural networks |
| url | https://ieeexplore.ieee.org/document/9214405/ |
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